The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
Content Distribution Systems
Content distribution systems offer a large variety of media items to the user for viewing. To enhance the user experience, content distribution systems may provide personal media recommendations to the individual user. One approach to recommending a media item involves determining media items of interest to the individual user based on the preferences of similar users using collaborative filtering or related techniques. One drawback of this approach however, is that a large amount of data needs to be stored and managed in order to determine similar users, making such an approach inappropriate in some situations.
Another approach is to identify media items that are similar to media items previously interacted with by the user. One implementation of such a recommendation system may involve attaching metadata tags to media items, such that media items having the same metadata tags are deemed to be similar. However, given the large variety of media available, thousands of metadata tags are needed to accurately describe the contents of media items.
Managing the metadata tags and performing similarity computations based on the multitude of tags usually needs large amounts of processing power and storage space. In addition, because metadata tags are often attached to a media item based on the perceptions of an individual, the tags may not accurately describe the contents of the media item, and, therefore, any similarity computation performed based on the tags may not be accurate. Further, the set of metadata tags that optimally determine similarity of media items and/or the extent to which each tag should contribute to this similarity typically varies depending on the actual content being evaluated, making global scoring functions based on metadata tags sub-optimal, and content specific ones difficult to estimate.